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全极化数据的可得,使得利用极化散射矩阵以减少多视全极化SAR图像中的相干斑成为了可能。其中,被称为最优的多视极化白化滤波器(MPWF)就是一个典型例子。然而,为提高滤波器参数估计的精度,同时能够自适应的检测图像中的结构特征,在滤波时采用适当的窗算法是必要的。本文提出了两个有效的窗算法,并用1994年NASA SIR-C/X-SAR,L band,经过四视处理的天山森林的极化数据进行了仿真。实验结果表明,这两种算法不仅很好的达到了降噪效果,而且对图像的纹理信息以及结构特征(即边缘特征、线性特征、强散射体特征)的保持效果是最佳的。
The availability of fully polarimetric data makes it possible to reduce the number of coherent spots in multi-view fully-polarized SAR images by using polarization scattering matrices. Among them, the so-called optimal multi-view polarization whitening filter (MPWF) is a typical example. However, in order to improve the accuracy of the filter parameter estimation and to detect structural features in the image adaptively, it is necessary to adopt the appropriate window algorithm in the filtering. In this paper, two effective window algorithms are proposed and simulated with the 1994 NASA SIR-C / X-SAR, L band, four-shot Tianshan forest polarization data. The experimental results show that these two algorithms not only achieve the good noise reduction effect, but also keep the texture information and the structural features (ie edge feature, linear feature, strong scatterer feature) of the image well.